Abstract
With the continuous development of network technology, cyberattack detection mechanisms play a vital role in ensuring the security of computers and network systems. However, with the rapid growth of network traffic, traditional intrusion detection systems (IDSs) are far from being able to quickly and accurately identify complex and diverse network attacks, especially those related to low-frequency attacks. To enhance the overall security of the Internet, an IDS based on hierarchical long short-term memory (HLSTM) networks is proposed. With the introduction of HLSTM, the network can learn across multiple levels of temporal hierarchy over complex network traffic sequences. The system is evaluated on the well-known benchmark data set NSL-KDD for comparison with other existing methods. The experimental results demonstrate that compared with existing start-of-the-art methods, our system has better detection performance for different types of cyberattacks. In addition, the low-frequency network attack types have higher classification accuracy and a lower false detection rate.
Highlights
Networks are increasingly integrated with people’s daily lives
Considering the above factors, this paper proposes an intrusion detection method based on hierarchical long short-term memory (HLSTM) [7]
To further improve the performance of intrusion detection systems (IDSs), an intrusion detection method based on HLSTM is proposed
Summary
Networks are increasingly integrated with people’s daily lives. With the rapid development and widespread application of the Internet and the Internet of Things (IoT), network security has gradually attracted the attention of enterprises and countries. Due to the large traffic volume and complex structure of network data, the processing capability of machine learning is limited. For this reason, traditional IDSs based on conventional machine learning methods generally have some shortcomings, such as a high false positive rate, poor generalization ability, and low real-time performance. The main contributions of this paper are summarized as follows: 1) A HLSTM-based IDS is proposed, that can learn across multiple levels of temporal hierarchy over complex network traffic sequences. III elaborates on the HLSTM-based intrusion detection model proposed in this paper, including the dataset used, data preprocessing method, HLSTM principle, and performance evaluation metrics.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
